前列腺癌分化与侵袭性：基于放射组学模型与 PI-RADS v2 评估的比较
Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2.
机构信息
Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China.
GE Healthcare Life Science, Shanghai, China.
出版信息
J Magn Reson Imaging. 2019 Mar;49(3):875-884. doi: 10.1002/jmri.26243. Epub 2018 Sep 19.
BACKGROUND
Multiparametric MRI (mp-MRI) combined with machine-aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics-based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI-RADS v2) scores.
PURPOSE
To develop and validate a radiomics-based model for differentiating PCa and assessing its aggressiveness compared with PI-RADS v2 scores.
STUDY TYPE
Retrospective.
POPULATION
In all, 182 patients with biopsy-proven PCa and 199 patients with a biopsy-proven absence of cancer were enrolled in our study.
FIELD STRENGTH/SEQUENCE: Conventional and diffusion-weighted MR images (b values = 0, 1000 sec/mm ) were acquired on a 3.0T MR scanner.
ASSESSMENT
A total of 396 features and 385 features were extracted from apparent diffusion coefficient (ADC) images and T WI, respectively. A predictive model was constructed for differentiating PCa from non-PCa and high-grade from low-grade PCa. The diagnostic performance of each radiomics-based model was compared with that of the PI-RADS v2 scores.
STATISTICAL TESTS
A radiomics-based predictive model was constructed by logistic regression analysis. 70% of the patients were assigned to the training group, and the remaining were assigned to the validation group. The diagnostic efficacy was analyzed with receiver operating characteristic (ROC) in both the training and validation groups.
RESULTS
For PCa versus non-PCa, the validation model had an area under the ROC curve (AUC) of 0.985, 0.982, and 0.999 with T WI, ADC, and T WI&ADC features, respectively. For low-grade versus high-grade PCa, the validation model had an AUC of 0.865, 0.888, and 0.93 with T WI, ADC, and T WI&ADC features, respectively. PI-RADS v2 had an AUC of 0.867 in differentiating PCa from non-PCa and an AUC of 0.763 in differentiating high-grade from low-grade PCa.
DATA CONCLUSION
Both the T WI- and ADC-based radiomics models showed high diagnostic efficacy and outperformed the PI-RADS v2 scores in distinguishing cancerous vs. noncancerous prostate tissue and high-grade vs. low-grade PCa.
LEVEL OF EVIDENCE
3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:875-884.
背景
多参数 MRI（mp-MRI）结合机器辅助方法在前列腺癌（PCa）诊断中显示出了较高的准确性和敏感性。然而，基于放射组学的分析尚未与前列腺影像报告和数据系统第 2 版（PI-RADS v2）评分进行彻底比较。
目的
开发和验证一种基于放射组学的模型，用于区分 PCa 和评估其侵袭性，与 PI-RADS v2 评分进行比较。
研究类型
回顾性。
人群
本研究共纳入 182 例经活检证实的 PCa 患者和 199 例经活检证实无癌症的患者。
场强/序列：在 3.0T 磁共振扫描仪上采集常规和扩散加权磁共振图像（b 值=0、1000 sec/mm ）。
评估
分别从表观扩散系数（ADC）图像和 T WI 中提取了 396 个特征和 385 个特征。构建了一个预测模型，用于区分 PCa 和非 PCa 以及高级别和低级别 PCa。比较了每个基于放射组学的模型与 PI-RADS v2 评分的诊断性能。
统计检验
通过逻辑回归分析构建基于放射组学的预测模型。将 70%的患者分配到训练组，其余患者分配到验证组。在训练组和验证组中均使用受试者工作特征（ROC）曲线进行诊断效能分析。
结果
对于 PCa 与非 PCa，验证模型在 T WI、ADC 和 T WI&ADC 特征上的 ROC 曲线下面积（AUC）分别为 0.985、0.982 和 0.999。对于低级别与高级别 PCa，验证模型在 T WI、ADC 和 T WI&ADC 特征上的 AUC 分别为 0.865、0.888 和 0.93。PI-RADS v2 在区分 PCa 与非 PCa 方面的 AUC 为 0.867，在区分高级别与低级别 PCa 方面的 AUC 为 0.763。
数据结论
基于 T WI 和 ADC 的放射组学模型均显示出较高的诊断效能，在区分癌性与非癌性前列腺组织以及高级别与低级别 PCa 方面优于 PI-RADS v2 评分。
证据水平
3 级 技术功效：2 期 J. Magn. Reson. Imaging 2019;49:875-884.
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